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High Dimensional Data and Multivariate Analysis (NDNS+)
| Credits |
8 credit points |
| Instructors |
Meulman, J.J. (Universiteit Leiden), Vaart, A.W. van der (Vrije Universiteit), Wiel, M.A. van der (Vrije Universiteit) |
| E-mail |
jmeulman@math.leidenuniv.nl, AW.van.der.Vaart@few.vu.nl, M.van.der.Wiel@few.vu.nl |
| Description |
This course gives an overview of techniques for analysing high-dimensional data, e.g. arising from microarray experiments, mass spectronomy, or high-throughput genotyping, including some statistical theory about the quality of such procedures. Keywords are:
- Classification.
- Multiple testing.
- Statistical learning.
- Support vector machines.
- Model selection.
- Cluster analysis.
- Regression trees.
- Boosting.
- Supervised and unsupervised learning.
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| Organization |
Lectures, reading, and presentations by the participants. |
| Examination |
Project or oral exam.
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| Literature |
- Hastie, Tibshirani, Friedman: The elements of statistical learning.
- Handouts.
- Research papers on applications in genetics.
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| Prerequisites |
No specific requirements. |
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